Abstract As a tropical cyclone (TC) approaches landfall, planetary boundary layer (PBL) processes are known to influence storm intensity and structure, but their role in TC track prediction remains unclear. Here we examine how PBL parameterization affects TC track prediction using Super Typhoon Saola (2023) as a case study. Simulated tracks using two PBL schemes are similar during the early stage but diverge markedly near landfall. Relative to the Yonsei University (YSU) scheme, the Mellor–Yamada–Janjic (MYJ) scheme produces a pronounced rightward track bias and delayed landfall, even with spectral nudging applied. Diagnosis of potential vorticity (PV) tendency shows that this bias arises from changes in diabatic heating (DH) and horizontal advection (HA). The MYJ scheme modifies asymmetric flow and convection in the lower–middle troposphere, while nudging alters the large‐scale steering flow in the middle–upper troposphere. These processes redistribute DH and HA contributions to PV tendency, producing a systematic rightward displacement of the TC track. These results demonstrate that PBL parameterization directly influences TC motion near landfall through its control of PV tendency and highlight the need to improve boundary layer representation in numerical weather prediction models.

A field campaign during Hurricane Fiona (2022) collected uniquely coordinated observations at the air-sea interface using paired dropsondes, Airborne EXpendable BathyThermographs, gliders, and saildrones. These platforms measured key atmospheric and oceanic parameters, including wind speed, temperature, and humidity, to quantify air-sea fluxes and characterize the atmospheric boundary layer and upper ocean. This study is the first to leverage these observations to evaluate the background ensemble at the air-sea interface from a self-cycled Hurricane Analysis and Forecast System data assimilation (DA) system. Verification against observations demonstrated that the background deterministic forecast captures key spatial and temporal variability of surface air temperature (SAT), humidity, wind speed, and sea surface temperature (SST), although systematic biases in humidity and SST were identified due to a combination of model and initialization errors. Evaluation of the 6-hr background ensemble showed skillful spread for SAT, humidity, and wind speed relative to the forecast error variance. In contrast, the SST ensemble spread was markedly underestimated and inversely related to forecast error variance, emphasizing the need for sampling the oceanic state uncertainty. For longer background forecasts, temporal cross-correlation of SST with SAT and surface wind speed revealed physically coherent time-lagged relationships, with atmospheric changes preceding SST responses by approximately 24-36 hr. Similar correlations were preserved in ensemble perturbations, reflecting the ability of the background ensemble in maintaining the dynamic consistency across the coupled interface. These findings provide insights into improving the coupled background ensemble covariances, paving the way for the development of strongly coupled ocean-atmosphere DA for hurricane forecasting.

This study integrates thermistor observations, hurricane glider measurements, and model-derived temperatures to examine the oceanic processes influencing Hurricane Laura’s rapid intensification, the role of the preexisting warm mixed layer at Stone Mooring (StM) in modulating cooling of the mixed layer, and the processes governing mixed layer heat evolution during and after storm. Prior to the storm’s passage, StM featured a 31°C warm mixed layer and elevated heat content (60–80 kJ/cm2), which strongly preconditioned the upper ocean. Thermistor data showed that Hurricane Laura produced a mixed layer cooling of 1.2°C on 26 August, compared with a model-estimated cooling of −1.04°C. A 1D shear-driven mixed layer model experiment indicates that, without the warm preexisting mixed layer, temperatures could have cooled down to 28.53°C, supporting the hypothesis that thermal structures associated with Loop Current warm core eddies rarely experience substantial cooling during hurricane passage. We also show that relatively small surface heat fluxes (5.04 kJ/cm2) sustained Hurricane Laura during intensification at StM. Mixed layer heat budget analysis shows that entrainment and surface heat fluxes were the primary drivers of the observed temperature tendency. These results improve understanding of upper ocean processes and demonstrate that observations from StM provide valuable constraints for operational hurricane models in the Gulf of Mexico.

A Scale-Aware Three-Dimensional Turbulent Kinetic Energy scheme (SA3DTKE) was developed 1) to incorporate three-dimensional shear, transport, and pressure diffusion in predicting turbulent kinetic energy (TKE) and 2) to homogenize the turbulent transport calculation used in large-eddy simulations with one-dimensional planetary boundary layer schemes used in large-/meso-scale models accounting for eddies across scales from small isotropic to large anisotropic turbulence. This study examines the impacts of the SA3DTKE scheme implemented in the Hurricane Analysis and Forecasting System (HAFS) model on tropical cyclone (TC) forecasts by analyzing HAFS simulations of hurricanes Kirk (2024) and Leslie (2024). Results show that SA3DTKE reduces the forecasting bias of track and intensity. SA3DTKE modulates the eyewall and rainband convection and thereby affects TC track via horizontal and vertical advection of wavenumber-1 potential vorticity asymmetries. SA3DTKE reduces the negative intensity bias through enhancing eddy diffusivity and TKE in the lower troposphere, leading to enhanced surface latent heating to maintain a stronger warm core structure. At the same time, stronger frictional force also generated stronger agradient forcing to facilitate the inward transport of absolute vorticity creating a stronger tropical cyclone denoted by the enhanced tangential wind and reflectivity. (c) 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The ocean supplies energy for tropical cyclones (TCs) and slows their winds through surface friction, which exerts a force on the ocean termed wind stress. The drag coefficient (Cd) is the key parameter that converts wind speed to wind stress and is currently estimated in forecast models from incomplete data collected in low-to-moderate ocean winds. Here, we use measurements from 11 Atlantic hurricanes to quantify Cd in winds up to 44 meters per second and surface waves up to 14 meters. It is found that Cd levels off, as wind speed surpasses 30 meters per second but does not decrease appreciably as suggested by previous indirect methods. Interaction of the wind and wave fields causes Cd to be 20 ± 2% higher on the motion-left side of a storm, where wind and waves are misaligned, than on the right. These results quantify directly a fundamental TC air-sea interaction parameter and demonstrate the importance of distinct TC quadrant-specific wind-wave interactions.

Accurate estimation of convective boundary layer height (CBLH) is vital for weather, climate, and air quality modeling. Machine learning (ML) shows promise in CBLH prediction, but input parameter selection often lacks physical grounding, limiting generalizability. This study introduces a novel ML framework for CBLH prediction, integrating thermodynamic constraints and the diurnal CBLH cycle as an implicit physical guide. Boundary layer growth is modeled as driven by surface heat fluxes and atmospheric heat absorption represented with the low tropospheric stability, using the diurnal cycle as input and output. TPOT and AutoKeras are employed to select optimal models, validated against Doppler lidar-derived CBLH data, achieving an R2 of 0.84 across untrained years. Comparisons of eddy covariance (ECOR) and energy balance Bowen ratio (EBBR) flux measurements show the same prediction capability. Models trained on the ARM SGP C1 site with ECOR data and tested at E37 and E39 yield R2 values of 0.79 and 0.81, respectively, demonstrating their adaptability. The ML model trained with all sites' data slightly enhances the performance compared with ML models trained over single-site data. The interquartile range for predicted CBLH is consistently narrower than that for DL-derived CBLH, reflecting lower variability in predicted CBLH compared to DL-derived CBLH, which is influenced by additional factors, which are not well represented with the model inputs. The model's generalizability across multiple sites at the ARM SGP site demonstrates its potential for transfer to greater distances, offering a scalable approach for enhancing boundary layer parameterization in atmospheric models.

Abstract This study investigates the vertical cloud microphysics in the stratiform sectors of Hurricane Tammy's outer rainband region and their relationship to local environments. We used in situ Optical Array Probe (OAP) measurements and tail‐Doppler radar data from four NOAA P‐3 spiral modules on 19–22 October 2023, as Tammy intensified from a tropical storm to a Category 1 hurricane. The four spirals sampled different environments, including embedded‐convective stratiform, mature stratiform, weak‐echo with mid‐level dry air entrainment, and near‐eyewall stratiform conditions. Vertical profiles of particle size distributions (PSDs), area ratios, and bulk parameters reveal distinct microphysical processes at different levels and under various environmental conditions. Above the melting layer, aggregation dominated in both intensification and mature phases. Riming was identified between −3 and −1°C layer during intensification, indicated by the increasing area ratios. During the mature phase, PSDs and bulk properties displayed vertical oscillations, suggesting contributions from secondary ice production. The near‐eyewall stratiform case contained more large particles above the melting layer. Across all four spirals, the melting layers exhibited a sharp decrease in hydrometeor number concentration and a transition to spherical hydrometeors, but their thicknesses varied substantially. Gamma‐fit parameter relationships differed among the four spirals, indicating environmental modulation of the total PSDs, whereas habit‐specific parameter relationships were controlled primarily by hydrometeor habit rather than by instantaneous temperature or horizontal wind speed. These observations highlight the need for high‐resolution in situ measurements to improve microphysics parameterizations in TC models, particularly for accurately representing habit‐dependent PSDs.

Satellite observations can reveal chlorophyll blooms in the wake of hurricane disturbances but their subsurface biogeochemical anomalies remain poorly described due to limited in situ observations. Here, we quantify the biogeochemical response across the ocean water column to Hurricane Idalia (2023) in the Gulf of America (also known as the Gulf of Mexico). We compile observations across the eastern Gulf using satellite data and two autonomous platforms: a profiling Biogeochemical-Argo (BGC-Argo) float and saildrone. Prior to the formation of Hurricane Idalia, an anomalously large extension of the Mississippi River plume spanned much of the eastern Gulf, contributing low-salinity and high-chlorophyll conditions. Following Idalia’s passage, the saildrone observed surface chlorophyll increases in the river plume extension, while the BGC-Argo float observed subsurface nitrate depletion and oxygen enrichment. These changes occurred as the float measured background ocean conditions evolving from the edge of the Loop Current to a cyclonic eddy, influenced by the river plume extension. Increases in chlorophyll concentration, decreases in nitrate, and elevated dissolved oxygen levels suggested increased primary production. BGC-Argo float observations revealed enhanced upwelling below the surface layer (~22 m) that shoaled the nitracline, fueling the increase in subsurface primary production (20–50 m depth). Our study provides a glimpse on the surface and subsurface ocean-biogeochemical changes associated with the Hurricane Idalia passage, highlighting the importance of the background mesoscale seascape on shaping the phytoplankton response to hurricane-induced disturbances. The combination of observations underscores the value of continuous in situ monitoring to better understand hurricane-driven impacts on the full ocean water column and the impacts these dynamics have on the base of the marine food web.

Abstract Low‐level clouds in the tropical cyclone (TC) rainbands modulate the radial inflow of warm, moist boundary layer air, which can impact TC intensity. Yet, precisely measuring rainband cloud boundaries with existing remote sensing techniques remains a challenge. To address this observational gap, this work leverages compact Raman lidar (CRL) backscattered power data collected by a NOAA P‐3 aircraft to distinguish between low‐level convective clouds, shallow clouds, stratiform precipitation, and clear air in the TC rainbands. CRL data are analyzed together with traditional tail Doppler radar (TDR) and in situ observations. An empirical CRL classification scheme finds that convective cloud, stratiform rainfall, and clear air sectors have unique kinematic and precipitation properties. A case study of Hurricane Sam (2021) demonstrates how this method resolves cloud structures with enhanced horizontal detail compared to the TDR. For the first time, the horizontal scales of low‐level convection and shallow clouds are determined. Environmental shear‐relative analyses reveal that the downshear‐left quadrant contains the most low‐level convective coverage and the least clear air. Meanwhile, the upshear‐right quadrant has little low‐level convection and plentiful clear air. Increasing environmental shear from low to moderate magnitudes amplifies these TC shear‐induced precipitation asymmetries. A conceptual framework is developed to highlight the unique information provided by the CRL when classifying cloud and precipitation structures.

Biases in tropical cyclone model forecasts impact track, intensity, and structural predictions. Yet, not all of these biases can be assessed using traditional comparisons with observations. This paper fills this research gap by introducing a new model evaluation framework that compares single forecasts to aircraft data from individual flights. Measurements from the novel compact Raman lidar are supplemented by dropsonde and tail Doppler radar data to explore how low-level tropical cyclone kinematics and thermodynamics evolve, particularly in the atmospheric boundary layer. The compact Raman lidar is essential in this framework, as it provides extensive thermodynamic data coverage in the tropical cyclone eye and rainbands. As a demonstration, observations are compared to short lead-time Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS)-Tropical Cyclone (TC) forecasts of Hurricane Sam (2021) over three sampling periods to diagnose model biases. Two important low-level biases consistently appear: The modeled rainband atmospheric boundary layer has a positive moisture bias, and the modeled low-level eye has a cold bias. Despite these consistent thermodynamic differences, Sam's primary and secondary circulations vary between flights, suggesting that mean tropical cyclone kinematics alone do not drive these temperature and moisture biases. This unique analysis framework focuses on individual flight legs, hinges on the high spatial density of the compact Raman lidar thermodynamic data, and provides an avenue for future model improvement via atmospheric boundary layer scheme adjustments. SIGNIFICANCE STATEMENT: Tropical cyclones rely on the transfer of heat and moisture from the ocean to the lower atmosphere to maintain their structure and intensity. Despite the importance of low-level thermodynamics, these fields are poorly constrained in tropical cyclone models. This study uses a new methodology to compare unique aircraft measurements of temperature and moisture with model forecasts for one storm. Key systematic errors in the model with respect to observations are found, including increased low-level moisture and colder temperatures in the eye of the storm. These model biases alter the characteristics of the simulated storm, partially explaining intensity forecast errors. In the future, if we can correct these thermodynamic biases by improving model physics, a more accurate depiction of tropical cyclones could be achieved.

As machine learning becomes more integrated into atmospheric science, XGBoost has gained popularity for its ability to assess the relative contributions of influencing factors in the atmospheric boundary layer height. To examine how these factors vary across seasons, a seasonal analysis is necessary. However, dividing data by season reduces the sample size, which can affect result reliability and complicate factor comparisons. To address these challenges, this study replaces default parameters with grid search optimization and incorporates cross-validation to mitigate dataset limitations. Using XGBoost with four years of data from the atmospheric radiation measurement (ARM) (Southern Great Plains (SGP) C1 site, cross-validation stabilizes correlation coefficient fluctuations from 0.3 to within 0.1. With optimized parameters, the R value can reach 0.81. Analysis of the C1 site reveals that the relative importance of different factors changes across seasons. Lower tropospheric stability (LTS, ~0.53) is the dominant factor at C1 throughout the year. However, during DJF, latent heat flux (LHF, 0.44) surpasses LTS (0.22). In SON, LTS (0.58) becomes more influential than LHF (0.18). Further comparisons among the four long-term SGP sites (C1, E32, E37, and E39) show seasonal variations in relative importance. Notably, during JJA, the differences in the relative importance of the three factors across all sites are lower than in other seasons. This suggests that boundary layer development in the summer is not dominated by a single factor, reflecting a more intricate process likely influenced by seasonal conditions such as enhanced convective activity, higher temperatures, and humidity, which collectively contribute to a balanced distribution of parameter impacts. Furthermore, the relative importance of LTS gradually increases from morning to noon, indicating that LTS becomes more significant as the boundary layer approaches its maximum height. Consequently, the LTS in the early morning in autumn exhibits greater relative importance compared to other seasons. This reflects a faster development of the mixing layer height (MLH) in autumn, suggesting that it is easier to retrieve the MLH from the previous day during this period. The findings enhance understanding of boundary layer evolution and contribute to improved boundary layer parameterization.

Convective cold pools (CPs) are inherent to mesoscale convective systems and have been identified in tropical cyclone (TC) eyewalls and rainbands. However, their distribution within TCs and their impacts on the TC enthalpy balance are not well understood. This gap is due to the scarcity of high-frequency observations over the ocean. By comparing 1-min data from Saildrone uncrewed surface vehicles to 10-min ocean moored buoy data, we demonstrate that the latter can detect CPs effectively. The analysis of the combined mooring-Saildrone dataset, associated with 241 TCs in the North Atlantic over the period 1998-2023, reveals that the frequencies of occurrence of CPs in the motion-right and shear-left quadrants are 50% and 30% higher than in the motion-left and shear-right quadrants, respectively. This indicates that there is enhanced convection in the motion-right and shear-left quadrants, and TC motion is more important than vertical wind shear in organizing CPs. Although, on average, CPs occur only about 6% of the time in TCs, their contribution to tropospheric latent heat release from their uplifting effect could be comparable to the total surface enthalpy flux in TCs under non-CP conditions. In addition, we found that CP gust fronts can boost surface sensible and latent heat fluxes by 65% and 11%, respectively, which can help low-enthalpy downdraft boundary air recover more quickly, increasing the readiness of the boundary layer for new convection under TC conditions. These findings suggest that properly resolving CP dynamics in TC models could improve the accuracy of TC intensity forecasts.

Tropical cyclone (TC) fullness, which measures the ratio of the annular width of the vortex skirt region to the total width of the outer wind field, is an important metric for characterizing TC wind structure and intensity change. High fullness is typically beneficial for intensification, but there are situations where high‐fullness TCs do not intensify. This study explores the structural characteristics and environmental conditions of weakening TCs under high fullness. Identifying these situations can be helpful for improved prediction of TC intensity change. This study analyzes 5,218 GPS dropsondes from 36 TCs to compare structural and environmental conditions of intensifying (IN) and weakening (WE) TCs under high fullness. For the environmental conditions, TCs tend to weaken with strong southerly vertical wind shear, dry air, and less ocean heat content, suggesting that more adverse conditions are required to offset the favorable effects of high fullness. In terms of structural characteristics, WE TCs have a warmer, moister boundary layer but weaker surface fluxes than IN TCs. Additionally, WE TCs exhibit stronger inward advection of absolute angular momentum within the friction layer. Above the frictional inflow layer, WE TCs display weak outflow, whereas IN TCs exhibit weak inflow. The implications of these differences in surface enthalpy fluxes and radial flow above the friction layer are discussed within the context of recent hypotheses regarding boundary layer ventilation and TC intensification.

Initially a Category 3 storm, Hurricane Ian (2022) rapidly intensified on the West Florida Shelf reaching Category 5 over the course of about 12 hr. Intensification occurred despite inhibiting factors such as high axial tilt, high vertical wind shear, low atmospheric moisture, and transit over a relatively shallow continental shelf. Using a high‐resolution simulation of Hurricane Ian from the Hurricane Weather Research Forecasting (HWRF) model, we examine the factors that both hindered and supported rapid intensification (RI) by blending various methods. We show that an increase in diabatic heating in the eyewall led to an inward radial advection of momentum, seen in both the absolute angular momentum budget and in the azimuthal wind budget. Analysis of the moist static energy budget indicates that the substantial latent heat flux from the surface was enough to balance heat losses through storm outflow. For instance, surface latent heat fluxes exceeded 1,500 W m −2 on the West Florida Continental Shelf. As suggested by actual ocean temperature observations that substantially exceeded those in the HWRF simulation, the latent heating may have even been larger. Physical explanations for discrepancies between the simulated Hurricane Ian and observations are provided, particularly those pertaining to the coastal ocean at the time of Ian's passage. This research provides a comprehensive explanation of the RI of a hurricane using momentum budget analyses as part of a coupled air‐sea analysis. Our findings demonstrate the importance of in situ oceanic air‐sea measurements in evaluating the performance of coupled models, especially for hurricanes.

On 15 September 2020, Hurricane Sally traveled within similar to 32 km from the location of Seaglider SG601 of the National Oceanographic and Atmospheric Administration/National Weather Service/National Data Buoy Center (NOAA/NWS/NDBC) in the northern Gulf of Mexico (GoM). Data from SG601 were used to examine the changes in the upper 100 m of the ocean under Hurricane Sally winds. In this study, we show a 0.5-1 degrees C cooling of the surface layer, recorded on the day of closest approach (DCA) of the glider to Hurricane Sally. We also found that freshwater from river discharge created an upper ocean barrier layer, which reduced the cooling of surface (or mixed) layer temperature by 38.6%. The high barrier layer potential energy (BLPE) together with the high buoyancy frequency squared (N2), prior to 15 September, indicated a very stable water column. Further analysis shows the interaction between SG601, Hurricane Sally, and a warm core Loop Current (LC) eddy in the northern GoM. Findings presented in this study also show that ocean models do not effectively simulate river discharge (plume) in the northern GoM.

Accurate prediction of tropical cyclone (TC) intensity remains a significant challenge partially due to physics deficiencies in forecast models. Improvement of boundary layer physics in the turbulent "gray zone" requires a better understanding of spatiotemporal variations of turbulent properties in low-level high-wind regions. To fill the gap, this study utilizes Anduril's Altius 600, a small uncrewed aircraft system (sUAS), that collected data in the eye and eyewall regions of category 5 Hurricane Ian (2022) at altitudes below 1.4 km. The highest observed wind speed (WSPD) exceeded 105 m s21 at 650-m altitude. The Altius measured turbulent kinetic energy (TKE) and momentum fluxes that were in good agreement with previous crewed aircraft observations. This study explores the scale-awareness turbulent structure by quantifying turbulence-scale (100 m-2 km) and mesoscale (2-10 km) contributions to the total flux and TKE. The results show that mesoscale eddies dominate the horizontal wind variances compared to turbulent eddies. The horizontal wind variances contribute 70%-90% of the total TKE, while the vertical wind variances contribute 10%-30% of the total TKE. Spectral and wavelet analyses demonstrate eddy scales from a few hundred meters up to 10 km, with unique distributions depending on where observations were taken (e.g., eye vs eyewall). These findings underscore the complex and multiscale nature of TKE and momentum fluxes in intense hurricanes and highlight the critical need for advanced observational tools within the high-wind hurricane boundary layer environment. SIGNIFICANCE STATEMENT: It is crucial to improve the understanding of turbulent processes in the low-level high-wind regions of tropical cyclones (TCs) for accurate intensity forecasts. Traditional data collection methods involving crewed aircraft are too risky to access these critical regions. This study demonstrates the use of a small uncrewed aircraft system (sUAS) to collect data at low levels within an intense Hurricane Ian (2022). The wind speed measured by the sUAS exceeded 105 m s21. Important turbulence parameters are estimated and presented as a function of wind speed, height, and radial locations. We found that mesoscale (2-10 km) eddies contributed to a significant portion of the total momentum transfer relative to turbulence-scale (100 m-2 km) eddies. This work demonstrates the usefulness of sUASs for improving the basic understanding of key physical processes in the high-wind hurricane boundary layer.

In the marine boundary layer, the exchange of momentum, heat, and moisture occurs between the atmosphere and ocean. Since it is too dangerous for a crewed aircraft to fly close to the ocean surface to directly obtain these measurements, a sUAS (small Uncrewed Aircraft System) is one of the only viable options. On 24 March 2023 a Black Swift Technologies S0 sUAS was deployed from the NOAA P-3 on a calm clear day off the west coast of Florida. For 23 min at the end of the mission, the sUAS flew 8 straight line legs with an average length of 2.15 km, at roughly 10 m above the ocean surface, with wind speeds between 3.0 and 4.5 m s-1. For the first time over the open ocean using a sUAS, the 4-Hz wind and thermodynamic data was used to calculate surface momentum flux, sensible heat flux, and latent flux using both direct covariance methods and the bulk aerodynamic formulas. Since all the flux quantities can be found using both direct and indirect methods, we are able to calculate the exchange coefficients of momentum flux (C D), latent heat flux (C E), and sensible heat flux (C H) with results that are generally in good agreement with previous studies over the same wind speed range. This study demonstrates the ability of sUAS to measure air-sea interactions. Future intention is to use sUAS to obtain similar measurements in high wind events such as hurricanes which could better help understand hurricane intensification and improve model physics.

The inner core of a tropical cyclone (TC) is vital for TC energetics and often undergoes dramatic changes. This article provides a review on the understanding and operational practices of the structural changes in the TC inner core, mainly focusing on recent literature and activities. The inner core structure of a TC is generally described as an axisymmetric vortex in the vicinity of a hydrostatic and gradient wind-balanced state. However, this schematic can sometimes be oversimplified. Recent studies have documented small-scale features of the inner core, structural changes in TC rapid intensification, secondary eyewall formation, and eyewall replacement cycles using observational data, and idealized and sophisticated models. In line with the progress in understanding the inner core structure, several operational agencies have recently analyzed TC structural changes using their subjective analyses or diagnostic tools, contributing to disaster prevention. We also discuss potential impacts of climate change on the inner core structure, for which further work is required to reach a solid conclusion.

Tropical cyclones (TCs) and hurricanes are among the strongest Mesoscale Convection Systems originating from the tropical oceans and can cause significant loss of lives and properties when landing. Prediction of TCs, especially their rapid intensification, remains challenging for numerical forecasts. Theoretical and modeling studies have shown that the surface turbulence heat flux fuels hurricane intensification, while the momentum flux or wind stress transfers the kinetic energy from the storm to the ocean to regulate the ocean mixing and stratification which in turn affect the Sea Surface Temperature and heat flux. The balance between the surface enthalpy flux (sum of sensible and latent heat flux) and drag plays a critical role in the TC and hurricane intensification. Due to the lack of direct observations inside the TCs and hurricanes, studies largely based on numerical models, lab experiments, air-deployed dropsondes, and indirectly from momentum budget analysis, have suggested a large deviation of wind stress and drag coefficients at high wind speed of > 20 m/s in TC and hurricane conditions. During the 2021-2023 hurricane seasons, a fleet of 5-12 Saildrone Uncrewed Surface Vehicles (USVs) have been deployed each year to intercept the TCs and hurricanes to make direct observations of the extreme air-sea interaction process. They provided real-time 1-minute averages of near-surface meteorology and ocean variables (5-minute for ocean currents) to hurricane forecast centers. This study utilizes the high-resolution 20-Hz data made available once the Saildrone USVs returned from their cruises after the hurricane season to investigate direct eddy covariance (EC) measurements of wind stress for a better understanding of the drag coefficients under TC and hurricanes. The directly observed drag coefficient, as well as the EC heat transfer coefficient (for sensible heat flux), will be compared to those used in the bulk flux algorithm (COARE) and in forecast models. Particular attention will be paid to the variations in different wind and wave conditions within the mesoscale system.

During the 2021-2023 Atlantic hurricane seasons, 24 Saildrone uncrewed surface vehicles (USVs) were deployed in the western Atlantic Ocean, Caribbean Sea, and Gulf of Mexico to collect ocean-atmosphere data within hurricane eyewalls. Sixteen different USVs intercepted tropical storms and hurricanes a total of 26 times, all with sustained wind measurements of at least tropical storm force (34 kt). Four USVs measured sustained hurricane-force winds (64+ kt) in the eyewalls of Hurricanes Sam (2021), Fiona (2022), Idalia (2023), and Lee (2023). An important advantage of the USVs compared to other observing platforms is that they can be actively steered into the paths of hurricanes and record data continuously during eyewall transects, enabling new insights into air-sea interaction processes in extreme conditions. This presentation gives an overview of the key observations and scientific results from the 2021-2023 missions. Direct measurements of the air-sea momentum flux and drag coefficient (Cd) from the USVs’ 20-Hz wind data show a distinct peak in Cd at wind speeds of around 40-50 kt and then a decrease and leveling off as winds approach 80 kt. These results extend findings from previous studies with direct covariance flux measurements, which were limited to winds of less than 50 kt. Measurements from the USVs also show diminished surface ocean cooling under the cores of Hurricanes Sam and Idalia due to strong upper-ocean salinity stratification, emphasizing the importance of salinity observations in the western Atlantic and Gulf of Mexico for potential improvements in hurricane intensity prediction.